II. Valuation
2. Forecasting corporate defaults
a) Country risk analysis
• Objectives
o identify common factors to measure a country’s political risk and financial risk;
o techniques used to measure country risk;
o how the assessment of country risk is used when making financial decisions.
• Definition: Country risk represents the potentially adverse impact of a country’s environment on the MNC’s cash flows.
• Country risk can be used:
o to monitor countries where the MNC is presently doing business;
o as a screening device to avoid conducting business in countries with excessive risk;
o and to improve the analysis used in making long-term investment or financing decisions
• Political Risk Factors
o Attitude of Consumers in the Host Country
Some consumers may be very loyal to homemade products o Attitude of Host Government
special requirements or taxes,
restrict fund transfers,
Funds that are blocked may not be optimally used
Currency Inconvertibility: MNC parent may need to exchange earnings for goods
subsidize local firms,
fail to enforce copyright laws.
o Political Risk Factors
War
Internal and external battles, or even the threat of war, can have devastating effects
Bureaucracy
Bureaucracy can complicate businesses
Corruption
Corruption can increase the cost of conducting business or reduce revenue
• Financial Risk Factors
o Current and Potential State of the Country’s Economy
A recession can severely reduce demand
Financial distress can also cause the government to restrict MNC operations o Indicators of Economic Growth
A country’s economic growth is dependent on several financial factors - interest rates, exchange rates, inflation, etc.
• Types of Country Risk Assessment
23
o A macro-assessment of country risk is an overall risk assessment of a country without consideration of the MNC’s business
o A micro-assessment of country risk is the risk assessment of a country as related to the MNC’s type of business
o The overall assessment of country risk thus consists of:
Macro-political risk
Macro-financial risk
Micro-political risk
Micro-financial risk
o Note that the opinions of different risk assessors often differ due to subjectivities in:
identifying the relevant political and financial factors,
determining the relative importance of each factor, and
predicting the values of factors that cannot be measured objectively.
• Techniques of Assessing Country Risk
o A checklist approach involves rating and weighting all the identified factors and then consolidating the rates and weights to produce an overall assessment
o The Delphi technique involves collecting various independent opinions and then averaging and measuring the dispersion of those opinions
o Quantitative analysis techniques like regression analysis can be applied to historical data to assess the sensitivity of a business to various risk factors
o Inspection visits involve traveling to a country and meeting with government officials, firm executives, and/or consumers to clarify uncertainties
• Developing A Country Risk Rating
o Assign values and weights to the political risk factors
o Multiply the factor values with their respective weights, and sum up to give the political risk rating
o Derive the financial risk rating similarly
o Assign weights to the political and financial ratings according to their perceived importance
o Multiply the ratings with their respective weights, and sum up to give the overall country risk rating
I. MEASURING POLITICAL RISK A. Country-specific perspective B. Political Stability
a. Frequency of government changes b. Level of violence
c. Number of armed insurrections d. Conflict with other states C. Economic Factors
1.Indicators of political unrest a. Rampant inflation
b. Balance of payment deficits c. Slowed growth of per capita GDP D. Subjective Factors
1. Profit Opportunity Recommendation 2. Political Risk and Uncertain Property Right 3. Business Environment Risk Index
4. Capital Flight
24
– Definition: the export of savings by a nation’s citizens because of safety-of-capital fears.
– Measurement: use the balance-of- payment account – Causes:
• Inappropriate economic policies
• Expectation of devaluation
• High political risk II. Economic and Political Factors:
1. Fiscal Irresponsibility
– high government deficits 2. Monetary Instability
– Money expansion
3. Controlled Exchange Rate System – currency usually overvalued 4. Wasteful Government Spending
– inability to service foreign debt – Relative size of government debt – debt to wealth ratio
5. Human Resource Base
– lack of strong work ethic
– Existence of government-imposed barriers to market forces – Amount of government-owned firms
– Amount and extent of corruption 6. Adjustment to External Shocks
– how well a nation responds
• Micro-perspective
o firm-specific perspective
o shortcomings of previous models o Weakness of Political Risk Models
assume all firms face the same risk o Expropriation
Is highly selective
Higher probability for extractive,
utility, and financial industries III. MANAGING POLITICAL RISK
A. Pre-investment Planning Four Policy Options a. Avoidance (no risk)
b. Insurance (shift risk) c. Negotiate environment d. Structure the investment B. Operating Policies
Five Post-Investment Policy Options:
o Planned Divestment
o Short-Term Profit Maximization o Changing the Benefit/Cost Ratio o Developing Local Stakeholders
o Adaptation: create a post-confiscation management contract
• Comparing Risk Ratings Among Countries
25
o One approach to comparing political and financial ratings among countries is the foreign investment risk matrix (FIRM)
o The matrix measures financial (or economic) risk on one axis and political risk on the other axis
o Each country can be positioned on the matrix based on its political and financial ratings
• Actual Country Risk Ratings Across Countries
o Some countries are rated higher according to some risk factors, but lower according to others
o On the whole, industrialized countries tend to be rated highly, while emerging countries tend to have lower risk ratings
o Country risk ratings change over time in response to changes in the risk factors
• Reducing Exposure to Host Government Takeovers
o The benefits of FDI can be offset by country risk, the most severe of which is a host government takeover
o To reduce the chance of a takeover by the host government, firms often use the following strategies:
o Use a Short-Term Horizon
This technique concentrates on recovering cash flow quickly o Rely on Unique Supplies or Technology
In this way, the host government will not be able to take over and operate the subsidiary successfully
o Hire Local Labour
The local employees can apply pressure on their government.
o Borrow Local Funds
The local banks can apply pressure on their government.
o Purchase Insurance
Investment guarantee programs offered by the home country, host country, or an international agency insure to some extent various forms of country risk
b) Rating agencies
This section summarizes the Standard & Poor’s approach to rate nonfinancial corporations.
Stand-alone credit profile
Business risk profile: risk/return potential for a company in the markets in which it participates, the country risks within those markets, the competitive climate within those markets (its industry risk), and the competitive advantages and disadvantages the company offers within those markets. The business risk profile affects the amount of financial risk that a company can bear at a given stand-alone credit profile and constitutes the foundation for a company's expected economic success. The assessments of country risk, industry risk, and competitive position are combined to determine a corporate issuer's business risk profile.
Business risk profile assessments range from "excellent" (highest) to "vulnerable" (lowest).
o Industry risk: competitive climate within those markets (scored 1-6)
Cyclicality: degree of revenue and profitability cyclicality
Competitive risk and growth environment
The effectiveness of industry barriers to entry;
The level and trend of industry profit margins;
The risk of secular change and substitution by products, services, and technologies;
26
The risk in industry growth trends
o Country risk: broad range of factors that can affect credit quality, which arise from doing business from or within a specific country
o Competitive position: advantages and disadvantages the company offers
Competitive advantage
Scale, scope, and diversity
Operating efficiency
Profitability
Financial risk profile: The financial risk profile is the outcome of decisions that management makes in the context of its business risk profile and its financial risk tolerances. This includes decisions about the manner in which the company is funded and how its balance sheet is constructed. It also reflects the relationship of the cash flows the organization can achieve, given its business risk profile, relative to its financial obligations. Cash flow/leverage analysis is used to determine a corporate issuer's financial risk profile assessment. Financial risk profile assessments range from "minimal" (least financial risk) to "highly leveraged" (greatest financial risk).
o Cash flow/leverage: pattern of cash flow generation, current and future, in relation to cash obligations is often the best indicator of a company's financial risk.
funds from operations (FFO) to debt
debt to EBITDA
payback ratios
cash from operations [CFO] to debt
free operating cash flow [FOCF] to debt
discretionary cash flow7 [DCF] to debt
coverage ratios
[FFO+ interest] to cash interest
EBITDA to interest Modifiers
Diversification/portfolio effect (for conglomerates): to capture the value of diversification or the portfolio effect for a company that has multiple business lines
o how meaningful the diversification
o degree of correlation in each business line's sensitivity to economic cycles
Capital structure
o Currency risk of debt o Debt maturity profile o Interest rate risk of debt o Investments
Financial policy: short-to-medium term event risks or the longer-term risks stemming from an issuer's financial policy
o over a longer time horizon, the firm's financial policies can change its risk profile based on management's appetite for incremental financial risk or, conversely, plans to reduce leverage
Liquidity: the sources and uses of cash
o potential for a company to breach covenant tests related to declines in EBITDA
7 „The money left over once all capital projects with positive net present values have been funded, and required payments (operational costs, such as wages) have been made.”
27
o ability to absorb high-impact, low-probability events o the nature of bank relationships
o the level of standing in credit markets
o the degree of prudence of the company's financial risk management
Management and governance: broad range of oversight and direction conducted by an enterprise's owners, board representatives, executives, and functional managers
o strategic competence o operational effectiveness o ability to manage risks
Comparable ratings analysis: issuer's credit characteristics in aggregate
Group or government influence: assessment of likely extraordinary group or government support (or conversely, negative intervention) factors into the issuer credit rating on an entity that is a member of a group or is a government-related entity.
identify the members of the group
determine a group credit profile
assess the status of an entity within the group and the resulting likelihood of support
and combine the entities' stand-alone credit profile with the support conclusion
five categories of group status:
o "core,"
o "highly strategic,"
o "strategically important,"
o "moderately strategic,"
o "nonstrategic."
Literature:
S&P (2014): How Standard & Poor's Rates Nonfinancial Corporate Entities. Standard and Poor’s Rating Serivces https://www.spratings.com/documents/20184/774196/HowWeRateNonFinCorps.pdf
c) Credit rating
Instead of taking a loan from a bank, companies and governments borrow money directly from investors by issuing bonds or notes. Investors purchase these debt securities such as municipal bonds expecting to receive interest plus the return of their principal. Credit ratings may facilitate the process of issuing and purchasing bonds and other debt issues by providing an efficient, widely recognized, and long-standing measure of relative credit risk. Credit ratings are assigned to issuers and debt securities as well as bank loans. Investors and other market participants may use the ratings as a screening device to match the relative credit risk of an issuer or individual debt issue with their own risk tolerance or credit risk guidelines in making investment and business decisions.
Credit ratings are opinions about credit risk. It expresses the rating agencies’ opinion about the ability and willingness of an issuer, such as a corporation or state or city government, to meet its financial obligations in full and on time. Credit ratings are not absolute measure of default probability. Since there are future events and developments that cannot be foreseen, the assignment of credit ratings is not an exact science. Credit ratings can also speak to the credit quality of an individual debt issue, such as a corporate or municipal bond, and the relative likelihood that the issue may default.
Ratings at S&P can be scaled as:
AAA: investment-grade with extremely strong capacity to meet financial commitments
AA: investment-grade with very strong capacity to meet financial commitments
A: investment-grade with strong capacity to meet financial commitments but somewhat susceptible to adverse economic conditions and changes in circumstances
28
BBB: investment-grade with adequate capacity to meet financial commitments, but more subject to adverse economic conditions
BB: speculative-grade with less vulnerable in the near-term but faces major ongoing uncertainties to adverse business, financial and economic conditions
B: speculative-grade with more vulnerable to adverse business, financial and economic conditions, but currently has the capacity to meet financial commitments
CCC: speculative-grade with currently vulnerable and dependent on favourable business, financial and economic conditions to meet financial commitments
CC: speculative-grade with highly vulnerable; default has not yet occurred but it is expected to be virtual certainty
C: speculative-grade with currently highly vulnerable to non-payment, and ultimate recovery is expected to be lower than that of higher rated obligations
D: speculative-grade with payment default on a financial commitment or breach of an imputed promise; also used when a bankruptcy petition has been filled or similar action taken
Cumulative Defaulters By Time Horizon Among Global Corporates, From Original Rating (1981-2018)
AAA AA A BBB BB B CCC Total
Number of issuers defaulting per time frame
One year 0 0 0 3 13 81 110 207
Three years 0 1 6 29 141 587 210 974
Five years 0 3 13 71 293 1,012 240 1,632
Seven years 2 6 28 102 399 1,231 256 2,024
Total 8 30 98 208 613 1,523 274 2,754
Percentage of total defaults per time frame (%)
One year 0 0 0 1,4 6,3 39,1 53,1
Three years 0 0,1 0,6 3 14,5 60,3 21,6
Five years 0 0,2 0,8 4,4 18 62 14,7
Seven years 0,1 0,3 1,4 5 19,7 60,8 12,6
Total 0,3 1,1 3,6 7,6 22,3 55,3 9,9
Source: S&P (2018): Default, Transition, and Recovery: 2018 Annual Global Corporate Default And Rating Transition Study. Standard and Poor’s
Literature:
https://www.spratings.com/en_US/understanding-ratings d) Traditional default forecast methods
Bankruptcy forecasting was initiated by the multivariate discriminant analysis of Altman (1968) as the Altman-Z model for public traded enterprises. Later on other approaches were published like the logit model of Ohlson (1980), Taffler’s (1984) modified Z and Zmijewski’s (1984) probit model. Since then, these are the most popular methods next to the neural networks and contingent claims analysis (Jackson – Wood 2013) and they provide similar results for the companies (Agarwal – Taffler 2008, Altman 2017).
The Altman-Z (1968) model was the first multivariate default-model for public-listed enterprises in the manufacturing sector – based on their liquidity, profitability and funding conditions. Later on, it was modified to study private firms as well (Altman 1977, Altman 2000), often referred as Altman-Z’:
Z′ = 0.717X1 + 0.847X2 + 3.107X3 + 0.420X4 + 0.998X5 X1 = (current assets − current liabilities) / total assets X2 = retained earnings / total assets
X3 = earnings before interest and taxes / total assets
29 X4 = book value of equity / total liabilities
X5 = sales / total assets
Companies under Z’<1.23 have 95% chance to go default in the next business years (it is 72% two years later and 48% three years later), while this chance is minimal above 2.9 (Altman 2000, Betts 1987, Kotormán 2009).
The original Altman-Z score has been modified many times in the last 50 years to fit private or non-manufacturing enterprises (Altman 2000). Despite its American origin, the model was successfully tested on different European samples: it was validated on 57% of the Slovakian construction industry (Rybárová et al. 2016), an N=521 Lithuanian sample was analysed between 2009 and 2013 (Marcinkevicius – Kanapickiene 2014) and nearly 60 thousand manufacturing and construction enterprises were compared between 2008 and 2013 (Karasa és Režňáková 2015). The banking sector was also a subject of different articles: international banks (N=34) between 2007-2010 (Altman et al.
2017) as well as public owned investment banks (N=34) were studied (Brou – Krueger 2016). The model was able to stand the test of big data analysis: samples like one thousand British enterprises between 2000-2013 (Almamy et al. 2016) or nearly nine thousand Czech companies with more than 10 employees (Machek 2014). The popularity of the method in the last two decades underlines its validity – however, some author (Tian – Yu 2017, Altman et al. 2017, Brou – Krueger 2016, Almamy et al. 2016, Grice – Ingram 2001, Wu et al. 2010, Qi 2014) suggests that a sectorial fine-calibration or the inclusion of macro-variables like inflation, interest rate or lending can enhance the predictive power ever further. The predictability of defaults one year earlier are varying on a narrow scale: 75 for Altman et al. (2017), 95-75% for Berzkalne – Zelgalve (2013), 74.5% for Marcinkevicius – Kanapickiene (2014), 88% for Salimi (2015) and 91% for Karasa – Režňáková (2015). Recession periods can bias the accuracy downwards according to Berzkalne – Zelgalve (2013).
The Ohlson-O model based on a logistic regression (Ohlson 1980), and it represents the probability of default within the next two years for P>0,5 under 96% reliability:
O=-1,32-0,407*log(TA/GNP)+6,03*TL/TA-1,43*WC/TA+0,0757*CL/CA-1,72*X-2,37*NI/TA-1,83*FFO/TL+0,285*Y-0,521*(NIt-NIt-1)/(abs(NIt)-abs(NIt-1))
𝑃 = 𝑒𝑂 1 − 𝑒𝑂 TA = total assets
GNP = Gross National Product price index level TL = total liabilities
WC = working capital CL = current liabilities CA = current assets
X = 1 if TL > TA, 0 otherwise NI = net income (pre-tax profit) FFO = funds from operations
Y = 1 if a net loss for the last two years, 0 otherwise
The Ohlson-O score has lower popularity in the literature: the Ebsco database accounts for 172 articles which is remarkably lower than the appearance of the Altman-Z score (N=2536). However, it can be converted to an exact default-probability instead of thresholds and the relative size of the company was involved to consider the too-big-to-fail effect as well as the cash-flow. This approach was mostly used to calibrate and backtest other more specific models on big data analyses: US pricing anomalies were studied by Novy-Marx (2013), Stambaugh et al. (2012) or by Charitou et al.
(2011).
The combined use of the Altman-Z and Ohlson-O methods was suggested by Dichev (1998) due to their different econometric fundaments (discriminant analysis and logit regression) and different calibration background (samples from the 1960’s and the 1970’s).
30 Literature
Altman, E. I. (2000): Predicting Financial Distress of Companies: Revisiting the Z-Score and Zeta models. Journal of Banking and Finance, 1, p. 1-51
Ohlson, J. A. (1980): Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18, p. 109-131
Additional literature
Agarwal, V. – Taffler, R. (2008): Comparing the performance of market-based and accounting-based bankruptcy prediction models. Journal of Banking and Finance, 32, 1541–1551. o.
Almamy, J. – Aston, J. – Ngwa, L. N. (2016): An evaluation of Altman's Z-score using cash flow ratio to predict corporate failure amid the recent financial crisis: Evidence from the UK. Journal of Corporate Finance, 36, 278-285. o.
Altman, E. I. – Haldeman, R. G. – Narayanan, P. (1977): ZETA Analysis: A New Model to Identify Bankruptcy Risk of Corporations. Journal of Banking and Finance, 1, 29-54. o.
Altman, E. I. – Iwanicz-Drozdowska, M. – Laitinen, E. K. – Suvas, A. (2017): Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman’s Z-Score Model.
Journal of International Financial Management & Accounting, 28, 131-171. o.
Altman, E. I. (1968): Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance, 23, 589-609. o.
Altman, E. I. (1983): Corporate Financial Distress: A Complete Guide to Predicting, Avoiding, and Dealing With Bankruptcy. Wiley-Interscience, John Wiley & Sons, Hoboken.
Altman, E. I. (2002): Corporate Distress Prediction Models in a Turbulent Economic and Basel II Environment. In Ong, M. (ed.): Credit Rating: Methodologies, Rationale and Default Risk, Risk Books, London, 1-29. o.
Altman, E. I. (2006): Corporate Financial Distress and Bankruptcy: Predict and Avoid Bankruptcy, Analyze and Invest in Distressed Debt. John Wiley & Sons, Hoboken.
Arellano, M. – Bond, S. (1991): Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies. 58, 277-297. o.
Berzkalne, I. – Zelgalve, E. (2013): Bankruptcy Prediction Models: A Comparative Study of the Baltic Listed Companies. Journal of Business Management, 6, 72-82. o.
Betts, J. – Belhoul, D. (1987): The Effectiveness of Incorporating Stability Measures in Company Failure Models. Journal of Business Finance & Accounting, 16, 361-383. o.
Blundell, R. – Bond, S. (1998): Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics. 87, 115-143. o.
Brou, F. B. – Krueger, T. M. (2016): Continental and National Differences in the Financial Ratios of Investment Banking Companies: An Application of the Altman Z Model. Journal of Accounting and Finance, 16, 37-49. o.
Charitou, A. – Lambertides, N. – Trigeorgis, L. (2011): Distress Risk, Growth and Earnings Quality.
Abacus, 47, 158-181. o.
Dichev, I. D. (1998): Is the risk of bankruptcy a systematic risk? Journal of Finance, 53, 1131-1147. o.
Grice, J. S. – Ingram, R. W. (2001): Tests of the Generalizability of Altman's Bankruptcy Prediction Model. Journal of Business Research, 54, 53-61. o.
Jackson, R. H. G. – Wood, A. (2013): The Performance of Insolvency Prediction and Credit Risk Models
Jackson, R. H. G. – Wood, A. (2013): The Performance of Insolvency Prediction and Credit Risk Models